Autonomous driving has quickly become an area of interest for vehicle manufacturers and navigation and mapping service providers. One particular area of interest is the use of computer vision to enable mapping and sensing of a vehicle's environment to support autonomous or semi-autonomous operation. Advances in available computing power have enabled this mapping and sensing to approach or achieve real-time operation through, e.g., machine learning (e.g., neural networks). As a result, one application of vision techniques in autonomous driving is localization of the vehicle with respect to known reference marks such as lane markings and/or other visible environmental features. More specifically, localization for autonomous driving generally requires a high degree of precision and accuracy. Accordingly, when localizing based on lane markings and/or performing other mapping or navigation related functions that depend on detected lane markings, service providers and manufacturers face significant technical challenges to enable efficient estimation of the quality of detected lane features to ensure that the lane data meet applicable quality requirements.